Commit 5e008df3 authored by Archan MISRA's avatar Archan MISRA

added references for 1st two paras; rephrased the comparison with WoWiFi

parent 507f56ed
......@@ -12,9 +12,9 @@
%anomalies in factories, city neighborhoods and critical infrastructure.
\emph{Energy} remains perhaps the greatest challenge in the pervasive
deployment of sensing systems, such as those used for human activities (e.g.
eating behaviour~\cite{XXX}, smoking~\cite{XXX}, or stress
levels~\cite{XXX}) or environmental sensing~\cite{XXX,YYYY}. In particular,
deployment of sensing systems, such as wearable devices for human activities (e.g.
eating behaviour~\cite{thomaz2015}, smoking~\cite{parate2014}, or stress
levels~\cite{ertin2011}) or embedded devices used for environmental sensing(e.g.,~\cite{campbell2014}). In particular,
sensors such as accelerometers or gyroscopes simply consume too much energy
to operate continuously without either a dedicated power source or a large
battery. However, using battery power introduces two distinct
......@@ -28,10 +28,8 @@ of sight (e.g., embedded inside factory equipment in industrial IoT
settings).
To overcome this disadvantages, many solutions using renewable energy
harvesting capabilities have been proposed. These include using ambient
light (via photovoltaic cells)~\cite{XXX}, temperature gradients (for
wearables with skin-contact)~\cite{XXX}, and kinetic energy~\cite{XXX}.
Each such technique is innovative, but has its own limitations--e.g.,
harvesting capabilities have been proposed--such as ambient light~\cite{hande2007}, temperature gradients~\cite{campbell2014b}
and kinetic energy~\cite{ryokai2014}. Each such technique is innovative, but has its own limitations--e.g.,
ambient light cannot be used for sensors mounted in poorly lit or occluded
locations (e.g., in a dark warehouse or on occluded body locations).
......@@ -43,27 +41,21 @@ Wireless charging, itself, is not novel, but current solutions require
either close proximity (3-5cm) to the transmitting power source (e.g., the
Qi~\cite{XXX} standard used by modern high-end phones), or can only charge
ultra-low power passive RFID tags~\cite{yeager2008} at longer ranges. More
recently, PoWiFi~\cite{talla2015powering} used WiFi to power an ultra-low
power wearable with a temperature sensor.
recently, PoWiFi~\cite{talla2015powering} demonstrated the use of WiFi, using multiple channels simultaneously, to power an ultra-low
power wearable (with a temperature or camera sensor), with low duty cycles.
\raj{we also power a cpu and a wifi transmitter. are those higher power than
the accelerometer? not sure why we focus just on the acelerometer and not
the fact we power a full system with a cpu, a sensor, and a wireless interface}
Our key scientific contributions are two-fold: we show (a) how to increase
the harvested WiFi power to much higher levels (O(100$\mu$W)) on an embedded
\emph{Our key scientific contributions are two-fold}: we show (a) how to increase
the harvested WiFi power (via directional WiFi transmissions) to much higher levels (O(100$\mu$W)), even on a single channel, on an embedded
device, at a much greater distance ($\sim$3-4meters from the transmitter)
than had been previously possible. This enables many more use cases, in
industrial IoT etc., and (b) that, with appropriate triggered-sensing
techniques, our solution can be used to sense and retrieve useful
gesture-related data from an accelerometer (a much higher-power sensor than
demonstrated in prior studies).
industrial IoT, smart homes, etc.; and (b) that, with novel triggered-sensing
techniques (that further extend the energy-driven intermittent sensing paradigm articulated in~\cite{hester20017b}), our solution can be used to collect useful gesture-related data from a batteryless, wearable sensing device, using an embedded accelerometer.
Our solution, called \names, uses beam-formed transmissions, by a
multi-antenna AP, of WiFi ``power packets'' (transmissions performed
explicitly to transfer RF energy) to deliver bursts of directed WiFi energy
to a client device. To form the beam towards the client, \name utilizes AoA
(angle-of-arrival) estimation techniques~\cite{XXX}. These AP-side
(angle-of-arrival) estimation techniques~\cite{XXX}\am{Jie:pls. provide a reference here and in .bib}. These AP-side
techniques are paired with novel energy-conserving features on the wearable
device, which activates its communication and sensing components
intelligently and selectively, to help capture only key events. While the
......@@ -73,9 +65,9 @@ paper presents a detailed design, implementation and empirical validation of
\noindent \textbf{Key Contributions:} To our knowledge, we are the first to
design and empirically demonstrate a working prototype (called \names) that
uses RF transmissions from a \emph{realistically-distant} WiFi AP to power a
uses RF transmissions, on a single channel, from a \emph{realistically-distant} WiFi AP to power a
batteryless, wrist-mounted wearable sensor device (which is collecting and
transmitting significant accelerometer data). To achieve this goal, we make
transmitting significant accelerometer data generated by regular human movement). To achieve this goal, we make
the following key contributions:
\begin{itemize}
......@@ -84,7 +76,7 @@ the following key contributions:
Through empirical experiments, it is clear that the harvested power, from a
conventional omni-directionally transmitting WiFi AP, is too low for
practical use: around $1-3\mu$W at distances of 3-4 meter. To tackle this
problem, we extend prior work~\cite{XXX}, to beamform the WiFi transmissions
problem, we extend prior work to beamform the WiFi transmissions
to spatially concentrate the transmitted power. Via experimental studies,
we show that even with real-world errors in direction estimation and
beamforming, directional transmissions with an 8-antenna array result in
......@@ -98,7 +90,7 @@ gesture-tracking applications. Such a wearable device, worn by a mobile
user, gives rise to two challenges: (i) the WiFi AP must be able to track
the wearable's changing location, without requiring constant active
transmissions from the wearable, and (ii) the power overhead of wearable
system, including the accelerometer, is XXX mW--while low, this is still
system, including the accelerometer, is XXX mW\am{Vu:pls. fill}--while low, this is still
higher than the harvested power of 700-800$\mu$W to permit continuous
sensing. To tackle both these challenges, the wearable employs a simple
kinetic energy harvester-based trigger to first detect \emph{significant
......@@ -119,8 +111,8 @@ to demonstrate the overall effectiveness of the proposed \name approach.
We believe that our work lays the foundational principles of a practical
WiFi-based energy harvesting mechanism for future embedded sensing devices
that have application beyond wearable devices (such as static sensors
deployed in commercial buildings or industrial warehouses).
that have application beyond just static sensors (e.g., in commercial or industrial sites) to
include wearable devices.
......
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